Physical Activity Analysis Using Baseline Deviation Detection
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Solution Overview
Problem
Existing physical activity measurement devices are limited in their functionality, primarily focusing on caloric aspects and step counting, and are not tailored for professional athletes or individuals with motor disorders like Parkinson's Disease, failing to provide comprehensive analysis of diverse physical activities such as biking, swimming, or addressing tremors at rest.
Innovation Solution
A system and method that analyze physical activity by comparing current measurements to a user's history and reference values, detecting deviations to recognize gestures and conditions, using sensors and processors to measure muscle pressure and orientation, and issue commands or provide condition feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing physical activity measurement devices are used for tracking caloric aspects and step counting, then the devices are suitable for regular people, but they are not tailored for professional athletes or individuals with motor disorders
Solution Approach 1:
The system dynamically adjusts measurement parameters and analysis methods based on the detected physical activity type. It identifies activity categories (e.g., cycling, swimming, boxing, skiing, writing, computer use, resting) and adapts the measurement focus accordingly - tracking power output for athletes, tremor characteristics for medical conditions, or basic activity levels for general users. This dynamic adaptation resolves the contradiction by making the system versatile across different user needs while maintaining measurement precision for each specific activity type.
Solution Approach 2:
The system changes the measurement parameters based on the physical activity type. Instead of using fixed step-counting or caloric-tracking parameters, it adjusts to measure relevant parameters such as muscle pressure, orientation, power output, or tremor frequency depending on whether the user is cycling, swimming, practicing boxing, or resting. This parameter adaptation enables the same device to serve professional athletes, individuals with motor disorders, and regular people effectively.
2Adaptability or versatility
If step counting and pulse rate measurement are used, then the devices work for general physical activity tracking, but they are irrelevant or insufficient for sport activities like biking, swimming, boxing, skiing or daily activities like writing, computer use, resting
Solution Approach 1:
The system segments the physical activity measurement into distinct activity categories and applies specialized measurement techniques to each segment. It identifies whether the user is engaged in sport activities (cycling, swimming, boxing, skiing) or daily activities (writing, computer use, resting) and applies the appropriate measurement protocol for each segment. This segmentation allows the system to maintain high measurement precision across diverse activity types while covering a broad range of possibilities.
Solution Approach 2:
The system achieves universality by implementing a multi-functional measurement framework that can handle various activity types with a single integrated platform. It uses sensors capable of measuring multiple parameters (pressure, orientation, motion) and automatically selects the relevant measurements based on the detected activity type. This multi-functionality enables the device to accurately track everything from professional athletic performance to subtle daily activities and resting states, resolving the contradiction between broad coverage and specific accuracy.
3Speed
If measurements are taken during physical activity, then real-time data is collected, but deviations from baseline require comparison with previous activities to recognize gestures and conditions
Solution Approach 1:
The system performs preliminary actions by establishing baseline measurements and reference values during periods of normal activity before the target event occurs. It continuously collects and stores activity history data to create a reference profile, which is then used to detect deviations during subsequent activities. This preliminary preparation of baseline data enables real-time detection of gestures and conditions by comparing current measurements against the established reference, thus maintaining both speed and information completeness.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing real-time measurements against historical data and baseline values. It provides feedback about deviations from normal patterns, which helps identify gestures and conditions. This feedback loop maintains real-time measurement capability while incorporating contextual information from activity history, as the system constantly references previous activities to interpret current measurements accurately.
Data Source
AI summary
A method of physical activity measurement and analysis, the method comprising computer-executed steps of: receiving at least one value extracted from measurements of a physical activity of a first user, and detecting a deviation of the physical activity of the first user from at least one previous physical activity using the received at least one value and at least one reference value calculated over at least one value extracted from measurements of the at least one previous physical activity.